Fig (Ficus Carica L.) Identification Based on Mutual Information and Neural Networks

Kattmah, Ghada; Azim, Gamil Abdel

doi:10.5815/ijigsp.2013.09.08

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…The Neural Networks is used with two hidden layers and one output layer with 3 nodes that correspond to varieties (classes) of FIG leaves. The proposal technique is a tester on a database of 84 images leaves with 28 images for each variety (class) [16]. In this work, the authors used Image Receptive Fields Neural Network (IRF-NN) for image recognition [17].…”

Section: A Back Groundmentioning

confidence: 99%

A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

Dara¹,

Satyanarayana²,

Govardhan³

2014

IJIGSP

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Many diversified applications do exist in science & technology, which make use of the primary theory of a recognition phenomenon as one of its solutions. Recognition scenario is incorporated with a set of decisions and the action according to the decision purely relies on the quality of extracted information on utmost applications. Thus, the quality decision making absolutely reckons on processing momentum and precision which are entirely coupled with recognition methodology. In this article, a latest rule is formulated based on the degree of correlation to characterize the generalized recognition constraint and the application is explored with respect to image based information extraction. Machine learning based perception called feed forward architecture of Artificial Neural Network has been applied to attain the expected eminence of elucidation. The proposed method furnishes extraordinary advantages such as less memory requirements, extremely high level security for storing data, exceptional speed and gentle implementation approach.

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Section: A Back Groundmentioning

confidence: 99%

A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

Dara¹,

Satyanarayana²,

Govardhan³

2014

IJIGSP

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“…[25] and [26] exist more information about the formulation of input and outputs. The accuracy of the system is calculated by using the following equation:…”

Section: Simulation and Discussionmentioning

confidence: 99%

Identification of Textile Defects Based on GLCM and Neural Networks

Azim¹

2015

JCC

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In modern textile industry, Tissue online Automatic Inspection (TAI) is becoming an attractive alternative to Human Vision Inspection (HVI). HVI needs a high level of attention nevertheless leading to low performance in terms of tissue inspection. Based on the co-occurrence matrix and its statistical features, as an approach for defects textile identification in the digital image, TAI can potentially provide an objective and reliable evaluation on the fabric production quality. The goal of most TAI systems is to detect the presence of faults in textiles and accurately locate the position of the defects. The motivation behind the fabric defects identification is to enable an on-line quality control of the weaving process. In this paper, we proposed a method based on texture analysis and neural networks to identify the textile defects. A feature extractor is designed based on Gray Level Co-occurrence Matrix (GLCM). A neural network is used as a classifier to identify the textile defects. The numerical simulation showed that the error recognition rates were 100% for the training and 100%, 91% for the best and worst testing respectively.

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“…satisfactory and improved classification results. Using reassignmen method, log-Gabor filters, a supervised method of classification (SVM) and a mutual information [41] gives the best discrimination between specific sound classes (92.07%).…”

Section: Comparaison Of State-of-the-art Methodsmentioning

confidence: 99%

On the Use of Time–Frequency Reassignment and SVM-Based Classifier for Audio Surveillance Applications

Sameh¹,

Zied²

2014

IJIGSP

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In this paper, we propose a robust environmental sound spectrogram classification approach. Its purpose is surveillance and security applications based on the reassignment method and log-Gabor filters.Besides, the reassignment method is applied to the spectrogram to improve the readability of the timefrequency representation, and to assure a better localization of the signal components. Our approach includes three methods. In the first two methods, the reassigned spectrograms are passed through appropriate log-Gabor filter banks and the outputs are averaged and underwent an optimal feature selection procedure based on a mutual information criterion. The third method uses the same steps but applied only to three patches extracted from each reassigned spectrogram. The proposed approach is tested on a large database consists of 1000 sounds belonging to ten classes. The recognition is based on Multiclass Support Vector Machines.

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Fig (Ficus Carica L.) Identification Based on Mutual Information and Neural Networks

Cited by 5 publications

References 14 publications

A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

A Novel Approach for Image Recognition to Enhance the Quality of Decision Making by Applying Degree of Correlation Using Artificial Neural Networks

Identification of Textile Defects Based on GLCM and Neural Networks

On the Use of Time–Frequency Reassignment and SVM-Based Classifier for Audio Surveillance Applications

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